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In this paper, we develop a novel method based on machine-learning and imageprocessing to identify North Atlantic right whale (NARW) up-calls in thepresence of high levels of ambient and interfering noise. We apply a continuousregion algorithm on the spectrogram to extract the regions of interest, andthen use grid masking techniques to generate a small feature set that is thenused in an artificial neural network classifier to identify the NARW up-calls.It is shown that the proposed technique is effective in detecting and capturingeven very faint up-calls, in the presence of ambient and interfering noises.The method is evaluated on a dataset recorded in Massachusetts Bay, UnitedStates. The dataset includes 20000 sound clips for training, and 10000 soundclips for testing. The results show that the proposed technique can achieve anerror rate of less than FPR = 4.5% for a 90% true positive rate.

Bioacoustic Signal Classification Based on Continuous Region Processing, Grid Masking and Artificial Neural Network